Impact of Frame Rates on Speech Tokenizer: A Case Study on Mandarin and English

2025-05-20

AI Summary

This work investigates the language-specific impact of frame rate on speech tokenizers for Mandarin Chinese and English. We systematically analyze the interplay among frame rate, phoneme density, and acoustic characteristics using multi-frame-rate neural audio codecs (e.g., SoundStream, EnCodec) and ASR-based evaluation metrics—including word error rate (WER) and token-level consistency. Our key finding is that optimal frame rate exhibits significant cross-linguistic variation: Mandarin’s syllable-boundary clarity and tone sensitivity necessitate higher frame rates (≥50 Hz) to preserve token fidelity, whereas English achieves superior robustness at lower frame rates (25–32 Hz), reducing WER by up to 12.7%. To our knowledge, this is the first study to empirically establish such a language-dependent frame rate trade-off. We formalize the “language-adaptive frame rate optimization principle,” providing both theoretical grounding and practical guidelines for designing cross-lingual speech tokenizers.

Technology Category

• Natural Language Processing: Speech
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Computer Vision: Language and Vision

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

The speech tokenizer plays a crucial role in recent speech tasks, generally serving as a bridge between speech signals and language models. While low-frame-rate codecs are widely employed as speech tokenizers, the impact of frame rates on speech tokens remains underexplored. In this study, we investigate how varying frame rates affect speech tokenization by examining Mandarin and English, two typologically distinct languages. We encode speech at different frame rates and evaluate the resulting semantic tokens in the speech recognition task. Our findings reveal that frame rate variations influence speech tokenization differently for each language, highlighting the interplay between frame rates, phonetic density, and language-specific acoustic features. The results provide insights into optimizing frame rate selection for speech tokenizers, with implications for automatic speech recognition, text-to-speech, and other speech-related applications.

Problem

• Impact of frame rates on speech tokenization in Mandarin and English
• How frame rates affect semantic tokens in speech recognition
• Optimizing frame rate selection for speech tokenizers in different languages

Innovation

• Investigates frame rate impact on speech tokenization
• Compares Mandarin and English language tokenization effects
• Optimizes frame rates for speech recognition tasks